基于灰色马尔可夫模型的物流园区物流量预测研究

物流量预测是物流园区规划和管理的一项重要内容.本文将灰色系统理论与马尔可夫链相结合,介绍了灰色模型-马尔可夫链预测物流园区物流量的预测方法,并对某物流园区未来三年的物流量进行预测,结果表明比单纯利用灰色模型进行预测更加准确可靠.     

基于Markov链在GDP增长率中的预测

GDP增长率直接反映了一个国家的经济发展状况。针对目前国内GDP增长率状况，本文通过马尔可夫链建立了模型，检验其正确性，并对以后的GDP增长率进行预测。     

马尔可夫分析法在企业应收账款管理中的应用

本文利用马尔可夫链状态转移矩阵对企业的应收账款进行分析、预测和动态监控,以期为加强企业对应收账款的管理提供参考。     

马尔可夫分析法在企业应收账款管理中的应用

本文利用马尔可夫链状态转移矩阵对企业的应收账款进行分析、预测和动态监控,以期为加强企业对应收账款的管理提供参考.     

供应链管理下基于供货不稳定的最优库存模型

针对供货环境的不稳定性,将t时刻系统所处的状态(是否能够供货)视为连续时间的马尔可夫链。在(s,S)存储策略下,利用两状态连续时间马尔可夫链的性质建立了一个新的存贮控制模型。在给定具体数值的基础上利用遗传算法对模型进行了检验计算,并对结果进行了对比分析。     

马尔可夫分析法在应收账款管理中的运用

在企业的管理决策等工作中，经常会遇到这样的情况，事物未来的发展及演变状态仅仅受事物现状的影响，而与过去的状态无关，也就是具有马尔可夫性。本文运用马尔可夫模型，对具有马尔可夫性的应收账款分析和预测，为马尔可夫分析法应用的拓广以及应收账款的管理和预测提供理论依据和实际应用的参考。     

企业战略外部环境预测评价

企业外部环境在很大程度上影响着企业战略的实施与结果，因此对企业战略外部环境的预测评价十分重要。文章利用马尔可夫链模型对企业战略外部环境进行预测评价，对整个企业战略绩效的评价提供前提基础。     

基于马氏链的供应链稳定性评价

从供应链运行过程中存在大量不确定因素,相应其产品的生产过程也表现出一定的波动性这一现象入手,根据生产过程具有显著马尔可夫性,以马尔可夫链理论为基础建立模型,对供应链稳定性进行测量评价。     

马尔可夫链在电力系统负荷预测中的应用

负荷的预测水平是衡量电力系统运行管理现代化的显著标志之一,负荷预测的结果除了由负荷本身的历史规律决定外,还受众多非负荷因素的影响,会因地区和气象而异,本文根据具体情况提出合适的负荷预测模型——马尔可夫链模型,具有重要的意义。     

灰色-马尔可夫链模型在股市预测中的应用

用GM(1,1)预测具有良好的精确性和规律性,但对于随机波动性较大的股市行业,它的预测精度比较低,而马尔可夫模型可以克服波动性较大的局限性,弥补灰色模型的不足,因此将两者结合起来对股市进行预测将能提高预测的精度。本文依据上交所20个月末收盘指数预测后四个月的月末收盘指数范围,实证分析表明灰色马尔可夫链模型在股市预测中应用的可行性     

MC's for MCMC'ists

We develop a minimum amount of theory of Markov chains at as low a level of abstraction as possible in order to prove two fundamental probability laws for standard Markov chain Monte Carlo algorithms:
1. The law of large numbers explains why the algorithm works: it states that the empirical means calculated from the samples converge towards their "true" expected values, viz. expectations with respect to the invariant distribution of the associated Markov chain (=the target distribution of the simulation).
2. The central limit theorem expresses the deviations of the empirical means from their expected values in terms of asymptotically normally distributed random variables. We also present a formula and an estimator for the associated variance.     

Some remarks on the construction of independent identically distributed random variables,Markov chains and martingales

In this note, we make some remarks on the construction of sequences of independent identically distributed random variables and of Markov chains concretely on a probability space (Ω,A,P). We also show that there are non non-trivial martingales of exponential type.     

一种基于马尔可夫链的随机预测模型——以日本地方经济为例

本文通过分析研究,提出一种基于马尔可夫链的随机预测模型,该模型在预测日本地方经济时可以取得较好的效果。该模型可以预测2020年及之前日本各地方县市经济的变化情况。虽然本文提出的模型比较简单,但通过扩展,该模型在预测日本地方县市经济时可以提供更准确的信息。     

Estimating Transition Probabilities from a Time Series of Independent Cross Sections

This paper considers the implementation of a nonstationary, heterogeneous Markov model for the analysis of a binary dependent variable in a time series of independent cross sections. The model, previously considered by M offitt (1993), offers the opportunity to estimate entry and exit transition probabilities and to examine the effects of time-constant and time-varying covariates on the hazards. We show how ML estimates of the parameters can be obtained by Fisher's method-of-scoring and how to estimate both fixed and time-varying covariate effects. The model is exemplified with an analysis of the labor force participation decision of Dutch women using data from the Socio-economic Panel (SEP) study conducted in the Netherlands between 1986 and 1995. We treat the panel data as independent cross sections and compare the employment status sequences predicted by the model with the observed sequences in the panel. Some open problems concerning the application of the model are also discussed.     

融合灰色理论与马尔科夫链的青岛市物流需求量预测

物流需求预测的准确性对物流基础设施投入和物流政策制定是至关重要的。根据物流需求受经济、社会发展和环境政策等不确定因素影响的特点,融合灰色理论与马尔科夫链,用灰色预测揭示系统时序变化的总体趋势,马尔科夫方法预测序列的随机波动的范围,能够优化灰色预测结果,提高预测的精度。以货运量为物流需求度量指标,建立灰色状态马尔科夫组合预测模型,对十二五期间青岛市物流需求量进行预测,预测结果可为青岛市十二五期间物流发展战略制定、物流服务体系构建和物流园区建设等工作提供理论参考和数据支撑。     

Validating an agent-based model of the Zipf׳s Law: A discrete Markov-chain approach

This study discusses the validation of an agent-based model of emergent city systems with heterogeneous agents. To this end, it proposes a simplified version of the original agent-based model and subjects it to mathematical analysis. The proposed model is transformed into an analytically tractable discrete Markov model, and its city size distribution is examined. Its discrete nature allows the Markov model to be used to validate the algorithms of computational agent-based models. We show that the Markov chains lead to a power-law distribution when the ranges of migration options are randomly distributed across the agent population. We also identify sufficient conditions under which the Markov chains produce the Zipf׳s Law, which has never been done within a discrete framework. The conditions under which our simplified model yields the Zipf׳s Law are in agreement with, and thus validate, the configurations of the original heterogeneous agent-based model.     

Mixed Hidden Markov Models for Longitudinal Data: An Overview

In this paper we review statistical methods which combine hidden Markov models (HMMs) and random effects models in a longitudinal setting, leading to the class of so‐called mixed HMMs. This class of models has several interesting features. It deals with the dependence of a response variable on covariates, serial dependence, and unobserved heterogeneity in an HMM framework. It exploits the properties of HMMs, such as the relatively simple dependence structure and the efficient computational procedure, and allows one to handle a variety of real‐world time‐dependent data. We give details of the Expectation‐Maximization algorithm for computing the maximum likelihood estimates of model parameters and we illustrate the method with two real applications describing the relationship between patent counts and research and development expenditures, and between stock and market returns via the Capital Asset Pricing Model.     

On the possibility of extinction in a class of Markov processes in economics

We analyze the possibility of eventual extinction of a replenishable economic asset (natural resource or capital) whose stocks follow a stationary Markov process with zero as an absorbing state. In particular, the stochastic process of stocks is determined by a given sequence of i.i.d. random variables with bounded support and a positive-valued transition function that maps the current level of the stock and the current realization of the random variable to the next period’s stock. Such processes arise naturally in stochastic dynamic models of economic growth and exploitation of natural resources. Under a minimal set of assumptions, the paper identifies conditions for almost sure extinction from all initial stocks as well as conditions under which the stocks enter every neighborhood of zero infinitely often almost surely. Our results emphasize the crucial role played by the nature of the transition function under the worst realization of the random shock and clarifies the role of the “average” rate of growth in the context of extinction.     

基于优化灰色-马尔可夫链模型的铁路货运量预测

在传统的灰色预测模型中,传统背景值公式和初始值的选取方法会造成模型产生偏差。通过对背景值公式的改进和初始值选取方法的变化,构造出预测精度更高的灰色模型,即优化GM(1,1),并将优化GM(1,1)与马尔可夫链预测相结合,对我国的铁路货运量进行了预测分析,得出了相应的预测区间及其发生概率。通过理论分析和算例表明,该方法的预测结果比传统灰色模型预测结果更加可靠。     

On the Bicentenary in St. Petersburg of Jacob Bernoulli's Theorem

We focus on the Bicentenary (1913) celebrations, organised in St. Petersburg, Russia, by the great probabilist, A. A. Markov, founder of the theory of Markov chains. This theory was stimulated by the need to demonstrate a Law of Large Numbers for sums of dependent random variables. Markov's work on the Law of Large Numbers originating in the celebrations was exposited and extended by S. N. Bernstein and, in English, by J. V. Uspensky, who had played an integral part in the St. Petersburg celebrations. Copyright © 2014 John Wiley & Sons, Ltd.